Sparse plane wave approximation in the low frequency domain introduces an assumption required for sparse analysis of room's complex wavefield which further opens the door to compressed sensing [1]}, [2]}. Mignot et al. [3]} have started the trend of the sparse modal analysis. They have designed a greedy approach which uses space decomposition based on iterative alternating projections for the estimation of the wave number and wave vectors that fully determine the acoustical behaviour of the given room. Due to the high dimensionality of data acquired by microphones, greedy methods such as Simultaneous Orthogonal Matching Pursuit (SOMP) [4]} (simultaneous, since we are fitting measurements from multiple microphones at once) have shown better performance than the relaxation of the minimization of \(\ell _0\)  norm [5]}.
